Genetic algorithm and forward method for feature selection in EEG feature space

• Autorzy: Rejer I. , Lorenz K.
• Języki publikacji: EN

Abstrakty

EN

There are a lot of problems that arise in the process of building a brain-computer interface based on electroencephalographic signals (EEG). A huge imbalance between a number of experiments possible to conduct and the size of feature space, containing features extracted from recorded signals, is one of them. To reduce this imbalance, it is necessary to apply methods for feature selection. One of the approaches for feature selection, often taken in brain-computer interface researches, is a classic genetic algorithm that codes all features within each individual. In this study, there will be shown, that although this approach allows obtaining a set of features of high classification precision, it also leads to a feature set highly redundant comparing to a set of features selected using a forward selection method or a genetic algorithm equipped with individuals of a given (very small) number of genes.

Słowa kluczowe

EN

brain computer interface; electroencephalography; feature selection; genetic algorithm; forward selection; SVM classifier

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Journal of Theoretical and Applied Computer Science
• Rocznik: 2013
• Tom: Vol. 7, nr 2
• Strony: 72--82
• Opis fizyczny: Bibliogr. 16 poz., tab.

Twórcy

autor

Rejer I.

• Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland

autor

Lorenz K.

• Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland

Bibliografia

• [1] Pfurtscheller G., Flotzinger D., Kalcher J. Brain-computer interface-a new communication device for handicapped persons. Journal of Microcomputer Application, Vol. 16, No. 3, 1993, pp. 293–299
• [2] Hammon P.S., de Sa V.R. Preprocessing and meta-classification for brain-computer interfaces. IEEE Transactions on Biomedical Engineering, Vol. 54, No. 3, 2007, pp. 518–525
• [3] Peterson D. A., Knight J. N., Kirby M. J., Anderson Ch. W., Thaut M. H. Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface. EURASIP Journal on Applied Signal Processing, No. 19, 2005, pp. 3128-3140
• [4] Lakany H., Conway B. A. Understanding intention of movement from electroencephalograms. Expert Systems, Vol. 24, No 5, 2007, pp. 295-304
• [5] Koprinska I. Feature Selection for Brain-Computer Interfaces. T. Theeramunkong et al. (Eds.): PAKDD Workshops 2009, LNAI No. 5669, 2010, pp. 100-111, Springer-Verlag Berlin Heidelberg, 2010
• [6] Dias N.S., Kamrunnahar M., Mendes P.M., Schiff S.J., Correia J.H. Feature selection on movement imagery discrimination and attention detection. Med Biol Eng Comput, Vol. 48, No. 4, 2010, pp. 331-341
• [7] Yom-Tov E., Inbar G. F. Feature Selection for the Classification of Movements From Single Movement-Related Potentials. IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 10, No. 3, 2012, pp. 170-177
• [8] Kołodziej M., Majakowski A., Rak J. R. A new Method of EEG Classification for BCI with Feature Extraction Based on Higher Order Statistics of Wavelet Components and Selection with Genetic Algorithms. ICANNGA 2011, Part I LNCS 6593, pp. 280-289, Springer-Verlag Berlin, 2011
• [9] Michalewicz Z. Genetic algorithms + data structures = evolutionary programs. Scientific and Technique Publishing House, Warsaw 1995
• [10] Koller D., Sahami M., Toward optimal feature selection, Proc. Machine Learning, pp. 284-292, 1996
• [11] Rejer I. Genetic Algorithms in EEG Feature Selection for the Classification of Movements of the Left and Right Hand. Advances in Intelligent and Soft Computing, Springer, 2013
• [12] Garrett D., Peterson D. A., Anderson Ch., Thaut M. H. Comparison of Linear, Nonlinear, and Feature Selection Methods for EEG Signal Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 11, No. 2, 2003, pp. 141-145
• [13] Data set III, II BCI Competition, motor imaginary http://bbci.de/competition/ii/index.html [dostęp: 2013]
• [14] Schlögl A., Lee F., Bischof H., Pfurtscheller G. Characterization of four-class motor imagery EEG data for the BCI-competition 2005. Journal of Neural Engineering, Vol. 2, No 4, 2005, pp. 14-22
• [15] Vapnik V., Statistical Learning Theory, New York: Wiley, 1998
• [16] Jain A.K., Duin R.P.W., Mao J. A Review, Statistical Pattern Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, 2000, pp. 4-37